The design and development of new engineered-products is a costly and challenging activity that is subjected to a high level of technical uncertainty. The uncertainties in product reliabilities are mitigated by dividing a complex product into carefully selected control volumes for which the interface parameters can be well defined. In doing so, however, designers can lose sight of the application-level, operating stresses, and their effect upon the control volume's intended performance and stresses.
Product reliability is an important aspect of any product development. Reliability is the probability that a material, component, or system will perform its intended function under defined operating conditions for a specific period of time. The conventional product development approach to reliability focuses on the following three main steps:                Failure Mode and Effect Analysis (FMEA);        Failure avoidance methods operating in parallel with product development; and        Reliability testing with large samples to obtain statistical confidence.        
Statistical methods rely on statistical models to predict reliability. However, in order for these models to be valid a significant sample size is required. This reliance on sample size places a major economic burden on qualification of industrial systems. As a result, most companies, not able or willing to pay for the complete test, simply deviate from the correct sample size while still using the statistical method. Instead of testing with the required sample sizes, companies design tests to fail with fewer samples and stop the test upon a few failures. This requires more complex statistical mathematics and greater costs associated with any form of population testing. In addition, statistical methods typically do not use trending information and normally work with sudden failures.
Furthermore, trends in performance and stress are often unclear, their real proximity to a defined failure stress is unknown and key stresses are not identified as such. Consequently, unexpected failures can occur during testing or after launching field applications, resulting in very high direct recall costs and brand devaluation.
In the 1960's Design Failure Mode and Effects Analysis (DFMEA) was conceived to reduce the frequency of unexpected failure. In this process a diverse group of experienced people (e.g. design engineers, service technicians and users) attempt to identify all of the potential failure modes, their probability of occurrence and potential consequences.
However, the DFMEA method is inherently flawed in that (a) the number of potential failure modes in a specific control volume can be extremely large, thus increasing the likelihood that some will be missed, and (b) the results from DFMEA do not provide the information needed by the designer to correct those failures that are identified.
The conventional approach starts with failure modes (i.e. FMEA, etc.) and assesses the risks based on experience. The present invention identifies the stresses in the product, identifies related failure mechanisms and their associated failure modes and generates engineering knowledge. The challenge in the conventional approach is that there are an infinite number of failure modes, but only a finite number of stresses. Focusing on failure modes expands development resources without significantly reducing product launch risks.
The present invention changes the focus of the product development process to generate objective engineering milestones that start with functions and lead to the failure mode. The present invention utilizes an integrated approach, using a strong engineering/scientific foundation to find a failure mechanisms based on design intent. Every step in the process provides another part of the puzzle to fully understand product life expectancy. Unlike failure modes, the present invention is focused on a specific part of the whole system, referred to as a control volume, where the number of stresses are often quite few. As such, the system can be readily analysed. The results of such analysis provide precisely the data required by the designer to correct his/her design for the whole system. Thus the present stress-based approach is much preferred over the previous conventional methods.